Pascal Thomas Salzbrenner^1*, Se Hun Joo¹, Lewis J Conway^1,2, Peter I C Cooke¹, Bonan Zhu³, Milosz P Matraszek⁴, William Charles Witt¹, Chris J Pickard^1,2*

1 Department of Materials Science & Metallurgy, University of Cambridge, Cambridge, U.K.

2 Advanced Institute for Materials Research, Tohoku University, Sendai, Japan

3 Department of Chemistry, University College London, London, U.K.

4 Trinity College, University of Cambridge, Cambridge, U.K.

* Corresponding authors emails: pts28@cam.ac.uk, cjp20@cam.ac.uk

DOI10.24435/materialscloud:44-c5 [version v1]

Publication date: Sep 28, 2023

How to cite this record

Pascal Thomas Salzbrenner, Se Hun Joo, Lewis J Conway, Peter I C Cooke, Bonan Zhu, Milosz P Matraszek, William Charles Witt, Chris J Pickard, Developments and further applications of ephemeral data derived potentials, Materials Cloud Archive 2023.150 (2023), https://doi.org/10.24435/materialscloud:44-c5

Description

Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction. This record provides the data associated with the different case studies demonstrating the uses of ephemeral data-derived potentials in the article "Developments and Further Applications of Ephemeral Data Derived Potentials".

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:dec82d1105dad330330880f088efc2de	7.8 KiB	README summarising and explaining the datasets
EDDP_paper_data.tar.gz MD5md5:97156c67ee4e1d151f690a57c8c329b8	488.5 MiB	Tarball containing the data for the paper

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

machine-learned interatomic potentials molecular dynamics phonons phase diagram superionicity hydrides metal-organic frameworks